After years of lagging behind other industries, cloud computing projects are now becoming commonplace in the finance sector. But while such initiatives have generally centred around non-critical systems such as email and customer relationship management (CRM), banks are set to start entrusting core systems to public cloud providers too.
According to John Schlesinger, chief architect at Temenos - which sells banking software to some of the world’s largest lenders - the majority of new core banking projects launched by the end of this decade will be in the cloud.
“We think that by 2020 most core banking initiatives will be in the cloud,” Schlesinger told Computerworld UK at the Temenos Community Forum in Lisbon this week. He claims it is a case of ‘when’ and not ‘if’ banks adopt the technology at the heart of their business. Temenos itself has already made headway with a small number of regulated and lightly-regulated micro-finance banks that are currently using its software as a service platform.
At this stage the number of banks running core banking in the cloud is small. But while adoption is currently low, it is an increase from just one percent the previous year, and Schlesinger expects that this will grow quickly.
“We believe that by 2020, which will be after PSD2 and after Instant SEPA, that the trickle will become a tsunami,” he said.
The first steps are already being made in the UK. Newly launched OakNorth was one to be the first UK bank to run its Mambu core banking systems in the cloud last year, migrating the platform to Amazon Web Services. Digital retail challenger bank Monzo, which built its core systems from the ground up using modern infrastructure software tools such as containers, also moved its core systems. Meanwhile, Metro Bank recently migrated its core infrastructure into a private cloud managed by Rackspace.
The approach makes sense for smaller challenger banks, lowering infrastructure costs and helping removing one of the barriers to entry in the market. “What cloud does is make everything cheaper. The on-premise story dramatically would lose out to the cloud story, given the economics of manufacturing of machines.”
“A new initiative today like Atom Bank or Starling Bank may not be in the cloud, but by 2020 it would be,” he added.
For the UK, regulation is no longer a barrier that prevents data from being held in the data centres of AWS, Azure and others, with the UK’s Financial Conduct Authority publishing guidance for lenders last year. “We don't think in the UK there is a regulatory problem, the problem is we are a very consolidated banking market, and for a bank to be in the cloud it would have to be a new initiative.”
Big Four held back by legacy infrastructure
For the Big Four UK banks the situation is different. Should they wish to move core systems out of their data centre in future, the main barrier is their reliance on complex legacy infrastructure, built up over decades in many cases.
“The big banks have a huge problem because for them it is a huge project to move their core banking,” Schlesinger said. “I think the only way they can do it is a build and migrate strategy and what they build should be built on top of infrastructure as a service. So over a fifteen-year period migrating core banking into the cloud.
“They’ll start the process in a couple of years’ time and they won’t finish it for a decade. So the data centres will be there running the existing systems but on a legacy basis.”
Cloud flexibility
There are a number of potential benefits of moving core banking to the public cloud, alongside lower costs. The flexibility of cloud computing helps lenders move more quickly to compete with nimble finch competitors, rather than relying on legacy infrastructure. There are also benefits around security for many banks, with the hyper scale cloud providers able to employ huge teams of security specialists that dwarf the number available to all but the biggest financial firms in order to protect system.
There are also numerous options in terms of routes to the cloud - IaaS, PaaS, SaaS and public, private, managed - and these will appeal to different banks depending on a range of factors, said Schelsinger. This includes the individual bank’s size and the geographies they operate in.
“I don’t think all the banks will do the same things,” said Schlesinger. “So for a tier one initiative, they will buy infrastructure and they will build platforms and software as service on it. That is the Tier 1 bank at home - so Chase in the US, or HSBC in the UK [for example]. But a Tier 1 bank out of geography looks more like a Tier 2 bank. I see Tier 2 banks renting platform as a service, and actually we are seeing that already in some of the RFPs we are getting.
"For all other banks, that's the Tier 3, 4 and 5, they will be buying software as a service, running very, very low entry costs."
In other news at the Temenos’ 18th annual European customer event, the Swiss vendor announced an update to its MarketPlace platform, launched last year to help connect bank and fintech firm services ahead of the PSD2 regulations.Temenos has added new functions to the MarketPlace such as a Sandbox platform as a service non-production environment to make it easier to test MarketPlace services.
Qualtrics has gone from the Silicon Slopes of Utah, building software for university researchers, to a platform of tools used by the world's biggest brands to survey their customers and employees.
As the story goes, Qualtrics started from the Smith family basement in Provo, Utah. Ryan Smith, cofounder and current CEO, started building research software as a project with his seriously ill father, a marketing professor at Utah's Brigham Young University (BYU), back in 2002.
Qualtrics started by targeting what Ryan's father knew best, the academic sector, something Smith joked about being "a horrible business model" because universities "have no money".
The original idea though was to create a Software as a Service (SaaS) tool that would allow anyone to conduct research quickly and easily. Then, as the world dealt with the fallout from the 2008 financial crash, Smith and his small team saw that customers were using the platform for something else: they had started to adopt the technology for customer experience management. In other words they were doing market instead of academic research on the platform.
Around 2010, as purse strings tightened and consumers became less brand loyal, businesses had to start competing more in the customer experience space. So although Qualtrics wasn't originally built for that, a trend was starting to form, and the team went about architecting the platform around this customer experience.
What is the Qualtrics Customer Experience Platform?
Smith told this founding story again on stage at the Qualtrics Converge Europe conference in London this week as a means of mapping how the company has evolved, as is par for the course in the tech world, into a platform – the Experience Management (XM) platform.
The journey is a familiar one: build the technology, adapt it to how customers are using it, and converge the platform back into an easy to understand package. Broken up into its component parts this consists of the customer experience, employee experience, product experience and brand experience products, all built on top of the core Qualtrics research engine.
The aim is to allow anyone within a business to gathering insights for things like customer or employee satisfaction, product testing, brand or pricing research; analyse the needs and trends, report it back and drive continuous improvement, all in a simple web-based platform.
Qualtrics wants its XM platform to be the repository for rich experiential data, instead of the static operational data businesses tend to run on. "So human factors, beliefs, emotions," Smith said. "Why things are happening and what will happen next."
Smith likes to benchmark Qualtrics against Salesforce, the SaaS leader in the customer relationship management (CRM) market. He claimed that the new experience management platform will be the "system of record for experience data".
The platform then has a whole host of smart features like sentiment analysis and statistical regression techniques to predict and push the highest priority cases to the right person to take action, not unlike Salesforce's Einstein features.
Read next: How Salesforce brought artificial intelligence and machine learning into its products with Einstein
Closing the experience gap
Smith joked on stage that you only have to look back a couple of weeks – cough, United – to see how devastating poor customer experience can be to a brand, and the importance of getting on top of what your customers think.
Smith called this the 'experience gap' and pointed to Bain and Company figures which show that 80 percent of firms believed they delivered a superior experience to their customers, but only eight percent of customers agreed.
Smith said that the Qualtrics platform, by bringing together the four core satisfaction elements of customer, product, brand and employees, helps companies close this gap.
"This is not just some new features," he said of the Experience Management platform. "We have been working on this for five years and how you manage those four core elements and how you delight customers by setting it up at every touchpoint and how you build iconic brands."
Qualtrics customer case studies
As the image below attests, Qualtrics has a seriously impressive stable of enterprise customers, and the pool is pretty vertical-agnostic.
Speaking on stage during Converge, Alison Windon from Allianz insurance said that following the arrival of a new group CEO, with a driving focus on the customer, the insurer "needed technology as a key enabler" to drive that cultural change.
"[Allianz needed] a tool to do heavy lifting for feedback, analysing it for key insights and facilitate key action planning at the client level," Windon said. "We needed to get the right information to the right people, at the right time, to drive impact. And we needed it to be flexible, self service and scalable globally. We found the right partner in Qualtrics."
After 18 months Allianz is now using Qualtrics across 22 geographies to get feedback from more than 12,000 customers.
Adidas took the opposite approach, in that it uses Qualtrics to take its expertise in customer experience to deliver a better employee experience.
Read next: How Adidas is bringing its customer experience approach to employee feedback
Qualtrics IPO?
Utah startups tend to raise less capital than those in Silicon Valley, which means they often grow at a slower, steadier pace. This genuine bootstrapping tends to give these companies a more genuine appreciation for their customers than the lip service you typically get from other B2B startups though.
Qualtrics now counts 8,500 customers globally and although progress has been a tad slower in Europe, it certainly has more of a presence than the average person might be aware of. After opening its first European office four years ago in Dublin, Qualtrics now has offices in Ireland, London, Munich and soon to be in Paris, supporting 1,200 European customers.
The money has followed, and in a major way, with the company securing a $180 million (£140 million) funding round earlier this month at a valuation of $2.5 billion (£1.94 billion) from Valley big hitters like Insight Venture Partners, Accel and Sequoia Capital, as it pushes towards growth and an eventual IPO.
Smith told TechCrunch in the aftermath of the funding round as much: "Going public is super easy to do. Just file the S-1 and we're out. It's about being public and how that works and getting the house in order to make sure that that's the case. We're going to be a great public company. We're going public."
Tommi Jaakkola, a professor of electrical engineering and comptuer science at MIT, tosses chocolates to students during 6.036 (Introduction to Machine Learning).
Photo: Lillie Paquette
Full Screen
Jaakkola teamed up with colleague Regina Barzilay, the Delta Electronics Professor of Electrical Engineering and __computer Science, to launch the course in 2013.
Photo: Lillie Paquette
Full Screen
This year, 700 students registered for 6.036 — so many that professors had to winnow the class down to about 500 students.
Photo: Lillie Paquette
Full Screen
Machine learning involves designing and building __computer programs that learn from experience for the purpose of prediction or control.
Photo: Lillie Paquette
Full Screen
During a typical class, Jaakkola covers multiple chalkboards with equations.
Photo: Lillie Paquette
Full Screen
The packed classroom includes about 40 graduate students from across campus, including the schools of architecture, engineering, management, and science.
Photo: Lillie Paquette
Full Screen
The class meets in MIT’s largest auditorium, Building 26-100.
Photo: Lillie Paquette
Full Screen
On an afternoon in early April, Tommi Jaakkola is pacing at the front of the vast auditorium that is 26-100. The chalkboards behind him are covered with equations. Jaakkola looks relaxed in a short-sleeved black shirt and jeans, and gestures to the board. “What is the answer here?” he asks the 500 MIT students before him. “If you answer, you get a chocolate. If nobody answers, I get one — because I knew the answer and you didn’t.” The room erupts in laugher.
With similar flair but a tighter focus on the first few rows of seats, Regina Barzilay had held the room the week prior. She paused often to ask: “Does this make sense?” If silence ensued, she warmly met the eyes of the students and reassured them: “It’s okay. It will come.” Barzilay acts as though she is teaching a small seminar rather than a stadium-sized class requiring four instructors, 15 teaching assistants, and, on occasion, an overflow room.
Welcome to “Introduction to Machine Learning,” a course in understanding how to give computers the ability to learn things without being explicitly programmed to do so. The popularity of 6.036, as it is also known, grew steadily after it was first offered, from 138 in 2013 to 302 students in 2016. This year 700 students registered for the course — so many that professors had to find ways to winnow the class down to about 500, a size that could fit in one of MIT’s largest lecture halls.
Jaakkola, the Thomas Siebel Professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society, and Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science, have led 6.036 since its inception. They provide students from varied departments with the necessary tools to apply machine learning in the real world — and they do so, according to students, in a manner that is remarkably engaging.
Greg Young, an MIT senior and electrical engineering and computer science major, says the orchestration of the class, which is co-taught by Wojciech Matusik and Pablo Parrilo from the Department of Electrical Engineering and Computer Science (EECS), is impressive. This is all the more so because the trendiness of machine learning (and, consequently, the class enrollment), in his opinion, is nearly out of hand.
“I think people are going where they think the next big thing is,” Young says. Waving an arm to indicate the hundreds of students lined up in desks below him, he says: “The professors certainly do a good job keeping us engaged, considering the size of this class.”
Indeed, the popularity of 6.036 is such that a version for graduate students — 6.862 (Applied Machine Learning) — was folded into it last spring. These students take 6.036 and do an additional semester-long project that involves applying machine learning methods to a problem in their own research.
“Nowadays machine learning is used almost everywhere to make sense of data,” says faculty lead, Stefanie Jegelka, the X-Window Consortium Career Development Assistant Professor in EECS. She says her students come from MIT’s schools of engineering, architecture, science, management, and elsewhere. Only one-third of graduate students seeking to take the spinoff secured seats this semester.
How they learn
The success of 6.036, according to its faculty designers, has to do with its balanced delivery of theoretical content and programming experience — all in enough depth to prove challenging but graspable, and, above all, useful. “Our students want to learn to think like an applied machine-learning person,” says Jaakkola, who launched the pilot course with Barzilay. “We try to expose the material in a way that enables students with very minimal background to sort of get the gist of how things work and why they work.”
Once the domain of science fiction and movies, machine learning has become an integral part of our lived experience. From our expectations as consumers (think of those Netflix and Amazon recommendations), to how we interact with social media (those ads on Facebook are no accident), to how we acquire any kind of information (“Alexa, what is the Laplace transform?”), machine learning algorithms operate, in the simplest sense, by converting large collections of knowledge and information into predictions that are relevant to individual needs.
As a discipline, then, machine learning is the attempt to design and build computer programs that learn from experience for the purpose of prediction or control. In 6.036, students study principles and algorithms for turning training data into effective automated predictions. “The course provides an excellent survey of techniques,” says EECS graduate student Helen Zhou, a 6.036 teaching assistant. “It helps build a foundation for understanding what all those buzzwords in the tech industry mean.”
Guadalupe Fabre, also a graduate student in electrical science and engineering and a teaching assistant, recommends 6.036 for people seeking to “develop a clear understanding of algorithms used in real life.” Fabre took the course himself as an undergraduate. “I learned to code and understand some of the latest algorithms used in machine learning,” he says. “I use a lot of the things I learned in my research.”
Be warned, however, that 6.036 teaches both theory and application, says Fabre, and grasping that combination requires hard work. “There is a risk of understanding one but not the other, and that can make the course challenging for some students,” he says. “If you want to impress interviewers with real knowledge about machine learning, take the course,” says Fabre. “However, if you are not willing to put in the time, don't take it. You are just going to stress out at the end.”
The majority of people taking 6.036 are willing to do the work, Zhou adds, crediting broad cultural excitement toward the applications of machine learning. “People in the class come from diverse backgrounds. I imagine they will apply these techniques in a wide variety of domains.”
Making it look easy
The comfort level — and charm — that Jaakkola and Barzilay display in the lecture hall is striking and goes a long way toward making their carefully designed course resonate with its huge audience. It helps dial back the impersonality that often comes with such numbers, students say.
In one of Barzilay’s recent classes, a volunteer solved an equation for k-means clustering, which involves the partitioning of data space, on the chalkboard at the front of the packed auditorium. After she correctly solved the equation, the class broke into spontaneous applause. “Wow, she solved that in front of 500 people,” shouted one student from the back of the room.
Rishabh Chandra, a first-year student who is an early sophomore in EECS, said the class size takes adjusting to. “It was hard to get beyond the first day,” he says, “but they do things to get people involved.” Half of the lectures are delivered by Barzilay and Jaakkola; additional faculty — this semester, Matusik and Parrilo — take care of the remainder.
Slipping from the same class a few minutes early to beat the rush, EECS junior Stephanie Liu, a front row regular, says Barzilay and Jaakkola have created a class that is detailed, well-structured, and even fun. “They teach really well,” she says. “And you’ve got to love the chocolates.”
Unibet parent company Kindred Group is turning to machine learning to recognise patterns of problem gambling across its suite of betting and casino websites.
Kindred Futures is the group's innovation wing, tasked with bringing in entrepreneurs and experts on how to "co-create the future of gambling," according to the organisation's website. It recently held a working group that brought in academics and figures from the technology industry to discuss how machine learning and AI might be able to help reduce problem gambling.
According to statistics from the Gambling Commission published by NHS Digital in 2012, 62 percent of all people gambled in that year, 0.5 percent of people identified as problem gamblers, and 4.3 percent of people were described as being “at low or moderate risk of developing problems with their gambling”. A combined report is scheduled to be published in spring this year.
Kindred Group already has a proprietary system in place – Player Safety Early Detection System, or PS-EDS – designed to raise red flags if players are thought to be at risk. The head of Kindred Futures, Will Mace, describes the existing system as "industry-leading" but also "rudimentary".
"It's a set of rules to do with behaviour – chasing losses, chasing wins, significant changes in your deposit pattern, that sort of thing," Mace explains. "But there can be loads of explanations for those – maybe you bet a couple of quid here and there, then all of a sudden you start betting hundreds of pounds. This would probably raise a flag, and we'd look to see if we can understand what's going on."
At present, the responsible gambling department make joint decisions on when they should intervene. Players aren't aware of the system until somebody contacts them.
"We record all transactional behaviour on our sites for obvious regulatory and operational reasons so when those things coincide and hit a rule we decide what to do about it," he says. "[But] if you were playing on our sites you wouldn't know anything that was happening, and then when a flag was raised you wouldn't know a flag was raised until we decided it was the right thing to do to get in touch with you."
Mace hopes that a machine learning-led approach that combines different data sets could augment the existing platform and even help automate the process to some degree.
A problem is that there are no set guidelines in place – everyone's circumstance is different, their habits will be different, and if they were really set on circumventing risk-reducing measures they could simply go to another website or walk into a bricks-and-mortar betting shop.
"User behaviour takes many forms – the interesting thing in this particular case is there's no single definition of problem gambling," Mace says. "What's a gambling pattern to me might be a problem but to you it might just be your normal form of entertainment. There is no 'if you do X and Y' you have a problem, everyone is different – that's why the more rudimentary methods have their shortcomings."
It's early days for Kindred Group, and Mace does not yet know what the platform will look like or if a machine learning integration will be successful. But the company is now in discussion with one of the startups it invited in for talks and is in the design phase.
Addressing problematic behaviour for online gambling is something the gaming companies will have to pay closer attention to whether they like it or not due to regulations from Europe, according to Mace.
"The regulators are increasingly insistent upon companies having the right, responsible gambling strategies, attitudes and approaches in order to both obtain and maintain a licence," Mace explains. "That's relatively new."
But for a truly effective approach to combating problem gambling, there would need to be some degree of collaboration across the betting websites. This might look like regulators pushing the operators to run a cross-website semi-anonymised way to pool their knowledge.
"It's definitely a challenge to an individual company like us trying to detect problem behaviour when you can just as easily bet with someone else," says Mace. "There's all sorts of challenges that we're taking steps towards seeing what we can do, and hopefully developing momentum from the industry to come together on it.
"Until there's collaboration across those sites nobody's going to have a complete picture of your betting behaviour, and therefore a lot of people who do have a problem are going to go unnoticed even if you bring in non-betting behaviour sources."
Adidas is taking its approach to gathering and responding to consumer feedback and applying it internally for employee feedback and satisfaction with a dynamic, monthly, mobile-first approach.
When director of people analytics Stefan Hierl started at Adidas Group, he found himself wondering why the company was using sophisticated resources to monitor consumer feedback but "archaic methods" internally. "That didn't make any sense to me," he says.
So Hierl went about moving away from the monolithic employee engagement survey of 80 questions, followed by months of analysis and reporting cycles. Now Adidas wants to create more of a constant feedback loop for its employees that better resembles the way it engages with customers.
Working with customer experience software vendor Qualtrics, Adidas is able to capture this feedback from a dedicated mobile app to gather net promoter score (NPS) data from employees.
The new approach is called "People Pulse" and is a fully branded mobile app for internal use, which runs on monthly cycles to capture both open text and 1-10 rating survey responses in a format that takes just five minutes to complete. This is important for a company like Adidas that has a lot of young employees who, according to HR Director Tony Cooke, "won't read email".
The flexibility of the Qualtrics platform means that Adidas can adapt the questions in People Pulse depending on changing business factors, like a change of CEO for example, on the fly.
Employees will be asked how likely they are to recommend working at Adidas and then two open-text questions on what is best and what could be improved about working there.
The respondent will then instantly see the results. "We provide real-time feedback to stakeholders and employees for confidence and trust that we are listening," Hierl says.
Adidas wants to continue to capture this sort of feedback at the appropriate point, and going mobile is a smart approach for this. This mirrors the consumer feedback model, where Adidas wants to get feedback at the appropriate touchpoint, such as straight after a purchase or delivery.
For employees this could be for feedback around the onboarding experience, which the company will request immediately via the app. "If you systematically capture this data you get more insight," Hierl explains.
The Qualtrics platform then performs text analysis on the results and clusters them according to keywords and sentiment. Managers can then easily digest the feedback through their own branded mobile app called Leadership Experience.
Hierl says that the results of People Pulse, internally known as the people score, is now considered a top KPI alongside revenue and share price.
Hierl would like to see the data capture side catch up to the modern day even further, going beyond form filling to more conversational interfaces like chatbots or AI personal assistants like Amazon's Alexa or Apple's Siri.
Customer trust and regulatory compliance capabilities are banks' biggest weapons when it comes to fending off a potential threat from fintech companies, according to Temenos CEO David Arnott.
Like all industries, from retail to media, the banking sector faces a significant challenge from new competitors that are heavily focused on delivering digital services. For banking, this means everything from peer to peer lender startups to tech giants such as Apple and Google moving into payments.
The situation is made all the more difficult for the big banks as they must contend with complex legacy technology and face a wave of regulations, such as GDPR and PSD2.
However, one of the reasons that we are perhaps yet to see the 'Uber-moment' for banks is that they have a number of advantages over their nimble competitors. Speaking at the Temenos Community Forum in Lisbon, Arnott said that banks should focus on their core strengths if they are to succeed in an increasingly digital market: namely, many years of experience providing financial services to customers while navigating a rapidly shifting regulatory landscape.
He said banks must "take advantage of modern technology, and, crucially, take advantage of the biggest asset that you have: the customers, the data, the platforms."
He added: "You have gone through the pain of the regulations which allows you to take deposits, you have a balance sheet that allows you to lend. We have seen people play around the fringes, but, whilst they will help you download music, we are not going to see Apple helping you out when it comes to paying for your first house.
"So you do have that lock-in, you do have that customer-centricity."
Arnott said that there are a number of other methods of dealing with the changes that are facing the industry as regulations level the playing field for new entrants into the market. For example, the incoming PSD2 regulation requires banks to open up their data to third party provides via APIs.
Read next: Open banking becomes a reality this year, what does it mean for banks, challenger banks, fintech startups and consumers?
However, these have various drawbacks. One is the traditional universal banking model, where a financial organisation 'manufactures' and distributes its own products over its own network, maintains its own regulatory environments and distributes through its own channels.
"The key here is today the majority of big banks are offering purely proprietary products," he said. "The challenge with this is where we see new entrants, who aren't necessarily taking market share. If you look two or three years ago, 90 percent of financial organisations were afraid of market share loss to fintech, today it is only 21 percent.
"What they are doing is impacting price. They are very disruptive, they have a different way of thinking, they come at subsets of ecosystems of financial services in a very different way, it is much more frictionless."
Another option is to become an "infrastructure player", providing lower-level services that others can build upon.
"You can become an infrastructure player, you can share the heavy burden of infrastructure costs, regulatory costs that you get running a financial organisation today with others," he said, adding that there are drawbacks to this approach too. "Ultimately though it is not a game-changer, it is a scale game. Yes, you can share some of your back-office costs, but ultimately it doesn't make you any different. There is no network effect, it is all about scale."
Another option is to become an aggregator of banking services.
"A fully fledged aggregator is somebody who pulls together, using their vast knowledge of a customer segment, a number of products that they don't own themselves and take a small term in recommending those products," Arnott explained. "It is like the TripAdvisor model, they used a network effect: vast volumes of recommendations matched with vast volumes of product and they sit in the middle and they take advantage of the network effect.
"The challenge is that this doesn't really take advantage of the key assets that we have today."
To provide customers with a range of options necessary to avoid these pitfalls, Arnott said banks require a modern technology infrastructure – a major challenge for some of the biggest banks – and be open to partnering and becoming a platform.
"First of all we need to provide modern banking infrastructure, so for transactions where the analytics goes straight through and we can give decisions quickly about key products," he said. "At the same you have to be open to the notion that a banking customer will want more than proprietary products. Certainly you offer a broader experience than the existing product set today."
While many banks have managed to keep pace with demand for new services such as mobile apps, this has generally meant delaying the inevitable requirement to modernise that tangle of legacy systems that many lenders own and operate.
"Putting digital channels on top of an ageing core is not enough," he said. "It will buy you a bit of time, but doesn't deliver value long term.
"There are those who understand it and those who don't, and they are spending fortunes on digital solutions on top of legacy and they are going to be caught out in a few years time, and I fear for those organisations."
Tommi Jaakkola is the inaugural Thomas Siebel Professor in EECS and IDSS.
Photo: Jason Dorfman/MIT CSAIL
Full Screen
Tommi Jaakkola, a professor of __computer science and engineering at MIT, has been named the inaugural holder of the Thomas Siebel Professorship in the Department of Electrical Engineering and __computer Science (EECS) and the Institute for Data, Systems, and Society (IDSS).
The appointment was announced by Anantha Chandrakasan, head of EECS and the Vannevar Bush Professor of EECS, and by Munther A. Dahleh, IDSS director and the William Coolidge Professor of EECS. “The appointment recognizes Professor Jaakkola's leadership in the area of machine learning and his outstanding mentorship and educational contributions,” Chandrakasan and Dahleh wrote in a message to EECS faculty. “Professor Jaakkola is internationally well-known in the fields of machine learning and natural language processing, as well as in computational biology. He is widely respected as an original researcher and has made high-impact contributions.”
The new professorship was established through the generous contribution of veteran software entrepreneur Thomas Siebel, chair and CEO of C3IoT. Siebel is well-known at MIT for having established the Siebel Scholars program, which annually provides support for 16 MIT graduate students (five in EECS, five in the Department of Biological Engineering, five in the MIT Sloan School of Management, and one focusing in energy science).
At the core of Jaakkola’s research are inferential and estimation questions in complex modeling tasks, ranging from developing the underlying theory and associated algorithms to translating such advances into applications. He has been a leading contributor to developing distributed probabilistic inference algorithms from this field’s inception to its current state as a well-established area of research.
From the modeling point of view, Jaakkola’s work covers a broad spectrum of areas, from the interface between generative and discriminative modeling, rethinking modeling from the point of view of randomization and combinatorial optimization, to recovery questions associated with continuous embedding of objects. In natural language processing (NLP), his contributions solving hard combinatorial inference problems such as natural language parsing, developing deep convolutional representations of text, and reframing complex models to reveal interpretable rationales for prediction. Several of his papers have received best-paper awards at leading events.
In addition, Jaakkola “has made outstanding educational contributions,” Chandrakasan and Dahleh noted. He established and oversaw the growth of the graduate machine learning course, teaching it for many years until Professor Leslie Kaelbling took it over for further development. Together with Professor Regina Barzilay, he developed the undergraduate machine learning course, which now enrolls more than 500 students per term. He modernized the advanced NLP course, again taught with Barzilay, from the point of view of neural approaches to NLP. In 2015, Jaakkola received the Jamieson Award for Excellence in Teaching in recognition of his educational contributions.
He has also made valuable professional contributions in his field and within EECS. He has held editorial positions on prestigious journals such as the Journal of Machine Learning Research and the Journal of Artificial Intelligence Research. He has also co-chaired or overseen areas of major conferences, including the Conference on Neural Information Processing Systems (NIPS), the Conference on Uncertainty in Artificial Intelligence (UAI), and the Conference on Artificial Intelligence and Statistics (AISTATS). He served for many years on the EECS Faculty Search Committee and has been a member of other committees as well. He has also contributed to the career paths of many students and postdocs that he has supervised and mentored at MIT. Former students and postdocs from his research group now hold positions in leading universities such as MIT, Carnegie Mellon University, and the University of California at Berkeley.
As an affiliate member of IDSS, Jaakkola has been instrumental in both the hiring and recruitment of statistics faculty as well as the creation of programs in statistics. He has served on the IDSS Statistics Faculty Search Committee from the start, and worked with the IDSS Statistics PhD Committee to develop a proposal for a dual PhD degree in statistics. He is also a participant in the Statistics and Data Science MicroMasters.
It's nearly time for another OpenStack Summit, the twice-yearly event where the community for the open source cloud platform meets to discuss progress, use cases, challenges and new features. This time round it'll be held in Boston.
There has been a general climb in users of OpenStack, and the word of the day will likely continue to be around containers.
Agatha Poon, research director for the APAC region at 451 Research, says she expects to see “a lot more real-world examples” of OpenStack deployments across regions, and in particular the commercial use of OpenStack in different verticals.
“On the product front, I would expect to hear more about the support for container-based deployment tools, application frameworks, and ecosystem,” Poon says. “And having achieved the platinum member status, I expect to hear a lot from Huawei at the summit in Boston.”
In particular, the emerging markets are a large growth area for OpenStack.
According to Poon, service providers are likely to continue to talk up container-based technologies but to progress there will need to be a consensus formed on the right use-cases for the customers.
“So far they talk about is to have the scale, you will probably use container-based technologies,” she says. “But other than that they’re trying to figure out what else they can do with container technologies.”
That might look like more details about containers not just at the infrastructure level but also how to support applications, and more details about the kinds of applications that will benefit from using container technologies, who’s doing that, and how far along they are.
Although adoption of OpenStack is on the up, the foundation has had some high-profile knocks of late.
Last year HPE significantly cut down funding for its OpenStack operations and Cisco followed suit shortly afterwards. HPE slashed its OpenStack employees, and Mirantis also cut its OpenStack engineers following its acquisition of TCP Cloud.
SUSE swooped in to acquire HPE’s OpenStack and Cloud Foundry assets from HPE, which was completed this year.
The high-profile departures raised questions about vendor commitment to OpenStack, leaving Rackspace as one of the few big Western vendors standing.
And just this month Intel slashed funding for the joint Intel-Rackspace OpenStack Innovation Center, an initiative that was just a few years old, and designed to accelerate the development and adoption of OpenStack in the enterprise.
Agatha Poon from 451 believes that enterprise use will continue to chug along. “They do need to spend a lot of investment and time to make it work for enterprise,” she says. “But for those who are actually down that path, I think they really like the openness of the platform, because a lot of the service providers believe the future way of developing services, allows them to develop microservices coming from OpenStack API-driven architectures.
“I think from that standpoint they’re still very tuned into that, but in terms of feature sets and capabilities or functionality, there’s always something they need to address.”
User survey highlights growth in adoption rate
The OpenStack Foundation has also released the twice-yearly OpenStack User Survey (PDF) ahead of next month’s Summit.
The Survey, which polled 1,300 users worldwide about their thoughts on the open source cloud platform, found that there were still stumbling blocks ahead. Although there was a rise in adoption, users polled said that installation was too complicated and that the lifecycle of releases needs further attention.
Users asked that the feature request process be made easier, and more transparency on delivery dates and when features will be made available. They cited concerns for installation particularly for TripleO and HA deployments, and said that a common deployment and lifecycle management tool would make life easier, along with the automation of installations and standardised automated deployment methods.
Containers were of particular interest to users, with 65 percent of those running containers inside OpenStack using the Docker runtime, and 47 percent of users orchestrating apps on OpenStack opting for Kubernetes.
A typical deployment, according to the survey, is at nine projects – and the average age of these deployments rests at just 1.68 years. Over half of the surveyed deployments started between 2016 and 2017.
See also: 451 cloud pricing report suggests OpenStack breakthrough
Users commented that the community is an especially important area where the open source platform stands up well. One user said in the report: “OpenStack has the benefit of thousands of developers all over the world working in tandem to develop the strongest, most robust, and most secure product that they can.”
Users also cited avoiding vendor lock-in as a main driver for running OpenStack, a point that vendors offering OpenStack services are particularly keen to highlight. Others said that being on the open source platform brought other competitive advantages like operational efficiency and, simply, saving cash – although these were not rated as highly as avoiding vendor lock-in.
User satisfaction in the survey seems to have taken a slight knocking, but this could be expected with the reported increase in new deployments.
SAP announced first quarter operating profits of €1.2 billion (£1 billion), up eight percent but coming in just below analyst expectations.
The German software maker also reported continued momentum in its cloud division, with new cloud bookings up 49 percent, accounting for €215 million (£183 million) in earnings. Total cloud subscriptions and support revenue grew 34 percent year-over-year to €905 million (£769 million).
S/4HANA adoption also grew again in Q1 2017, with the company adding approximately 400 new customers, almost 50 percent of which were net new to SAP. It now counts more than 5,800 customers using its next generation ERP platform.
Read next: SAP responds to S/4HANA roadmap criticism with launch of 'navigator' tool
Traditional software and support still dominates the balance sheet though, accounting for revenues of €3.4 billion (£2.9 billion) in Q1, up eight percent year-on-year.
CEO Bill McDermott said: "SAP's outstanding first quarter results are a decisive follow-on to our record setting 2016. Led by S/4HANA, we are seeing mass customer adoption of our solutions globally."
Business outlook
SAP reiterated its annual outlook following the announcement of full-year 2017 non-IFRS operating profit to be in a range of €6.8 billion (£5.8 billion) to €7.0 billion (£5.9 billion) at constant currencies.
These Q1 results follow a strong close to 2016 posted in January, after a slow start to that financial year. The company eventually met its annual targets comfortably with total operating profits up four percent to €6.6 billion.
SAP finished 2016 with cloud subscriptions and support revenue up 30 percent to €2.9 billion for the full year. Classic software licences and support revenue was only up a modest three percent for the year to €15.4 billion.
There's a wealth of useful apps designed for IT professionals out there offering remote server access, Wi-Fi scanners and community forums (to name a few). We've recommended 14 handy iPhone apps to try this year.
Some of the apps listed are older but still offer a great resource for IT pros, while others are more recent and are definitely worth a try.
This list will be regularly updated so get in touch if we've missed one that you love.
Read next: 11 best free and open source inventory management systems
1. Spiceworks Help Desk
Spiceworks, a well-known online community for IT professionals offers a mobile app enabling access to its community forums and discussions, for free.
This iPhone app, used by over 1 million IT pros worldwide, enables users to view their network inventory and open a ticket for any existing issues, offer help to others in the community and update information on their Spiceworks account.
Download Spiceworks.
2. Pingdom
Pingdom offers IT pros insight into the uptime, downtime, and overall performance of their website.
Pingdom will view the current status and response times for each site of the monitored sites and provide notifications, root-cause analysis and quick switches between accounts, if needed.
Download Pingdom.
3. ITmanager.net
ITmanager.net offers an all-in-one tools for network administrators to securely monitor and manage a variety of enterprise-level servers including VMware, Citrix XenApp and XenDesktop, XenServer, Windows, Microsoft Exchange, Hyper-V, Active Directory, SSH, Telnet, Amazon Web Services and Apple Remote Desktop (to name a few).
ITmanager.net will view, analyse and remotely monitor virtual machines and environments, reset passwords, manage routers, switches, network attached storage devices and printers, manage applications and provide a task scheduler, DNS manager and command prompt for Windows servers.
Simply, Mocha VNC lets you connect to a Mac or Windows PC and view its files and programs on your iPhone.
Mocha VNC includes fast Mac OS X login, encrypted password login, a camera barcode scanner, 8 -and 32-bit colour modes, local mouse support and standard VNC (virtual network computing) protocol.
Download Mocha VNC.
5. Speedtest.pro
Speedtest.pro enables users to test the speed of their Wi-Fi connection and add results to a general database for analysis in (they claim) under 20 seconds.
This app tracks past speeds, calculates how long file transfers will take and compares the data with other networks.
It's a simple app, but very useful to determine what's slowing a network down and calculating how much time should be set aside for large data downloads or transfers.
Download Speedtest.pro.
6. iWork
iWork is a suite of applications designed for Apple users aiming to replace the need for Microsoft Office.
iWork includes Pages, Keynote and Numbers which allow users to create a variety of documents on their iPhones. All documents can be saved via iCloud and can be easily accessed across all Apple devices.
RSA SecurID is a two-factor authentication application that generates one-time passwords which lets you use a PIN and OTP to access your documents and data.
This app is based on 'Token' software which you'll need to import separately by using a QR code.
Additionally, you'll also need a RSA Authentication Manager or the RSA SecurID Authentication Engine API to be able to use this app for software token provisioning and user authentication purposes.
Download RSA SecurID.
8. NetworkToolbox
Comprised of 32 individual tools, this iPhone app scans and analyses local or public networks for security issues or incorrect configurations.
Scanning options include Wi-Fi network IP scanning, port scanning and SHODAN scan engine integration which exposes online devices, their country, port, network and hostname.
Other notable tools include FTP and SFTP clients, IP calculation, HTTP browser, mail server check, socket analysis and terminal tool, ping IP or domain and map search.
Download NetworkToolbox.
9. LANScan
Developed for network administrators and IT managers, LANScan offers a Wi-Fi network scanner.
The tool provides information about all the devices on a local network, allowing users to drill down into the network providing TCP Connect scanning, ARP scanning, port scanning, information on devices broadcasting Bonjour services, reverse DNS and remote 'wake' devices configured to LAN.
Download LANScan.
10. LogMeIn
LogMeIn offers a secure pathway between desktop PCs, its files and your iPhone by providing remote access to PCs and Macs over Wi-Fi or mobile data.
LogMeIn users can access both home and work computers, access and edit __computer files from your iPhone and remotely run any application on your __computer via your iPhone.
Download LogMeIn.
It's important to note that users of this app will require a subscription to LogMeIn Pro. More here.
11. CamScanner Pro
CamScanner Pro for iPhone enables users to essentially turn their phone into a portable scanner. Using CamScanner Pro, users can take a photo of a document, save it as an image file or PDF and share them with colleagues via email or the cloud, print or fax.
Ideal for receipts, notes, invoices, meeting notes, business cards and certificates, this app can integrate with Dropbox, Google Drive, Evernote, OneDrive and Box and sync across multiple devices.
CamScanner does offer a basic version for free with 200MB of cloud storage for up to 10 people and a premium option providing an additional 10GB of cloud data for up to 50 people.
Download CamScanner Pro.
12. Adobe Acrobat reader
The Adobe Acrobat reader enables users to view, annotate and share PDFs, for free. Users can comment on files, offer feedback and sign forms with via Adobe’s file reader. This app is ideal for any IT pro that collaborates often, requiring instant viewable feedback.
Adobe Acrobat reader offers a few subscription levels from £7.99 to £69.99.
For those working with Microsoft's Active Directory, Active Directory Assist enables users to view and manage users, groups, and computers from their iPhone.
Users can remotely can reset passwords, edit user accounts and group memberships and perform other admin tasks. Active Directory Assist supports compatible with Windows 2003, Windows 2008, Windows 2008 R2 and Windows 2012 and can directly connect to the Active Directory server via Wi-Fi or VPN.
Download Active Directory Assist.
14. Network Utility
Network Utility enables users to check their web server over the internet and locate network information such as internal and external IP, network name, MAC address, gateway address, subnet mask and DNS addresses.
Offering a real-time map, users can visualise PING response times and other real-time statistics.
Data science and machine learning are fully entrenched terms in the enterprise space in 2017, with more and more organisations building out data science teams to drive real business value.
With help from the DataIQ 100, a list of the most influential people in data-driven business, Computerworld UK asked a range of top data leaders what trends they are seeing for the year ahead:
1. IoT data
João Pela, a data scientist at RotaGeek and former CERN employee sees the growth of data by sensors as a huge opportunity for businesses. He says: “Internet of Things is becoming smarter, and so smart objects are making their way into the mainstream consumer space.
"Machine learning is key to unlocking the full potential of this field: in the future, not only will we all have a personal assistant to help with everyday situations, the smart objects will themselves know how and when we like to use them (Google Nest, for example, is already making way in this field)."
Liz Curry, business process manager at Comic Relief agrees: "The amount of data that’s available because of the Internet of Things means that it’s possible to find more hidden insights than ever before."
2. Hiring
iStock Photo: Yuri Arcurs
One challenge for businesses is accessing the necessary skills to make the most of the large volumes of informations available to the. "We are seeing the increasing struggle to find the right people with the background, skills and real world experience for these roles," says Jessica Kirkpatrick, a data scientist at Hired. "With many seeking traditional backgrounds such as data science and statistics, companies are being forced to compete over a limited, highly skilled talent pool, especially at a senior level."
Read next: How to get a job as a data scientist: What qualifications and skills you need and what employers expect
Kirkpatrick advises that businesses get creative when it comes to hiring for data science roles. This means "looking outside traditional requirements and to alternative backgrounds. By seeking smart candidates in areas such as science, finance and business who have an aptitude in problem solving, businesses can give themselves access to a wider pool of untapped talent who have a foundation that they can build on."
3. GDPR considerations
Liz Curry, business process manager at Comic Relief believes that regulation will have a huge impact on the way businesses do data science this year. She says: "With the introduction of the GDPR in May 2018, I think the challenge to data analysis will be to ensure that we’re transparent about what we’re doing with our customers’ data whilst still trying to get actionable insights from them to benefit the sector."
4. Spreading data science through the business
iStock Photo: Gilaxia
Andrew Day, chief data officer at Sainsbury’s wants to see data science approaches reach more corners of the business.
"Where most organisations have been talking about the use of data science in solving customer and marketing-related problems, the step change will come in using maths ubiquitously across the organisation – in product development, pricing, ranging, site location, logistics and so on," he says.
"As a consequence, everybody involved in the management of a business will need to become familiar with the art of the possible and expect deploying mathematical and data-led solutions to become part of the day job."
5. Embedding data scientists within product teams
iStock Photo: Dolgachov
Vince Darley, VP for growth at Deliveroo has a similar view for the year ahead, and sees a structural change on the horizon.
"This year I think the biggest shift will be in how broadly many businesses will begin to see the benefits of data science, and how that will be driven by more businesses changing from a single central data science team to a more productive, impactful structure where data scientists are embedded across the business in many, many different operational and product teams."
Read next: Best data science tools: Data science platforms for modelling and deploying machine learning and predictive algorithms
6. Image recognition techniques
Gideon Mann, head of data science at Bloomberg sees image recognition as the boom area in data science communities this year. "There is so much technology developed right now which hasn't been deployed. I think over the next year the deployment of that existing technology will be huge and if I was to bet on an area of growth it would be image recognition."
Read next: How Bloomberg is using machine learning and data science to keep users hooked to its terminals
This follows the open sourcing of Google's TensorFlow machine learning software library, which was developed in house to help the search giant improve the way it categorises and organises image files.
7. Driving prescriptive analytics
Kjersten Moody, vice president, information and analytics at Unilever says: "Focus in 2017 will be on joining up structured and unstructured data to use in prescriptive analytics."
Read next: How to use data scientists and machine learning in the enterprise
In short, businesses like Unilever are looking to move beyond just running analytics on their data to giving business users actual, prescriptive insights to improve business outcomes.
Drones are so popular now that there's a UK dronecode: a simple set of rules to let you know where you can and can't fly one. We'll also list places where you're not allowed to fly - such as the Royal Parks.
We'll also explain the equivalent rules in certain other countries in case you want to take your drone on holiday to capture some great aerial video.
If you don't have a drone yet, then check out the best drones to buy and then our guide on how to fly a drone.
You can also jump straight to drone laws in Europe
UK Dronecode
Until their recent boom in popularity, drones were lumped in with ‘small unmanned aerial vehicles’ on the CAA's website and you had to try to figure out which rules applied to modern quadcopters.
Now, the site has a page dedicated to drones which outlines the most important rules. This is the basic Dronecode:
Keep your drone within your line of sight and at a maximum height of 400ft (122m)
Make sure your drone is within 500m from you horizontally
Always fly your drone well away from aircraft, helicopters, airports and airfields
If fitted with a camera, a drone must be flown at last 50m away from a person, vehicle, building or structure not owned or controlled by the pilot.
Camera-equipped drones must not be flown within 150m of a congested area or large group of people, such as a sporting event or concert
Many quadcopters, including DJI’s Phantom 4, are capable of flying much higher than the limit, so it’s easy to unwittingly break the law. The reason for choosing 400 feet, according to the CAA, is because this is generally what is measured as the limit of normal, unaided sight.
Horizontally, the limit on flying is 500 metres from you – considerably further than 400ft. In practice, it's easy to lose track of a drone at around 200-250m away from you. The important thing is to make sure you can see the drone you're controlling as you're responsible for it.
As long as you abide by these rules, you won’t get into trouble. There have only been a few cases so far of drone owners being prosecuted and they typically involve people blatantly flouting the rules.
Where can I fly a drone in the UK?
Unless you have a huge back garden it's usually impossible to fly your new drone because of limited space and the potential for crashing, but your neighbours could also make a complaint – especially if your drone has an obvious camera.
You may well be able to fly in your local park, but always check before you fly. Some parks have signage which explains what is and isn't permitted. You might see a 'no model aircraft' sign, which also includes drones.
London
All eight of London's Royal Parks are no-drone zones, as are many of the commons including Wimbledon Common, Putney Common, Clapham Common. You're not allowed to fly any model aircraft or even a kite at these sites.
You can fly on the heaths such as Hampstead Heath and Blackheath, although this may not be the case for long as these spaces, too, are under pressure to restrict the use of drones.
In the borough of Lambeth, you will have to have a commercial licence to fly as hobbyists are considered no different from commercial operators.
In Hackney, you need to fill out an application form.
Chelsea is a 'congested area' so you cannot fly there at all. It's the same for Lewisham, Dagenham, Barking and Redbridge.
In Bexley, drones are banned from all parks and open spaces.
You can fly in parks in Ealing, though.
Greenwich, Barnet and Camden don't have a drone policy, but as mentioned, you can't fly in Greenwich Park.
In Islington and Sutton, just be careful to fly without causing a nuisance. This is a much more sensible policy that banning drones from all parks open spaces: as it effectively means you cannot fly.
If you're unsure, check with the local council before flying. There's still confusion in some areas about whether drones are permitted or not, so don't be surprised if you can't get a clear answer.
Other restricted areas
These are the places we know about - if your local park or open space has restrictions, let us know.
All parks and open spaces in Derby are now no-fly-zones.
Although extremely unhelpful because of it's lack of clear guidance, the Lake District's website appears to suggest that you can fly drones under 20kg in the National Park.
Bye-laws in the Peak District National Park mean you cannot fly drones. The website is a clearer, explaining you can't fly in the park and you must obtain permission from any land that isn't part of the National Park, such as on National Trust land.
It's not permitted to fly a drone in the New Forest either.
Can I fly over someone's land?
You need permission from the owner of the land if you will take off or land on their property.
Flying over someone's property is more of a grey area. Currently, the rights of a property owner are restricted in relation to the airspace above his or her land to such a height as is necessary for the ordinary use and enjoyment of his land.
In other words, you can fly in the airspace over their land (but not higher than the general rule of 400ft) as long as you do not cause a nuisance, infringe their privacy or otherwise interfere with the "ordinary use and enjoyment" of the land.
It would be down to a a judge to decide whether or not a drone pilot was infringing these rights, should a case go to court.
UK no-fly zones
Of course, there are some areas you cannot fly at all, such as near airports, power stations and military bases.
These are called no-fly-zones and there's an app, the NATS Drone Assist, which is available for Android and iOS. This requires you to sign up for an account with an email address, rather than being a simple map overlay.
As well as restricted airspace, the app displays ground 'hazards' such as powerlines, railway lines, schools, petrol stations and other areas where you should be cautious of flying.
It also shows areas, such as parks, where you must be careful of flying near people congregating.
Assuming you're satisfied that it's ok to fly somewhere, you must still obey the minimum and maximum distance rules of the Dronecode.
Do I need a permit or to register my drone?
No, you don't need to register your personal drone or get a permit for a recreational drone in the UK. If you're planning to use your drone for paid work, however, that's a different story, and you will need Permission for Aerial Work, which has to be renewed annually. You can find out more on the CAA's website.
The law may be different in other countries. Sweden, for example, now requires drone owners to acquire a permit before flying - as the government deems the drones 'surveillance devices', even if they don't have a camera installed.
The UK government is proposing to change the regulations so that any recreational drone weighing more than 250 grams has to be registered. Ministers also want drones to be 'electronically identifiable' on the ground so their owners can be tracked.
They are also proposing increases to the maximum fine for flying a a no-fly zone, which is currently limited to £2,500. Should the law change following these proposals, we'll update this article.
Drone safety and insurance
The final part of the dronecode is to fly safely. Each flight is your responsibility, which means you are liable for any damage caused by your drone. It’s worth checking if your home insurance covers this and, if not, get a dedicated policy.
You don't have to have drone insurance by law, but it's a good idea. It costs around £35 per year and there are lots of providers (just search drone insurance UK). These will give you personal public liability insurance which will protect you against claims if you crash into and damage someone's property or injure someone with your drone.
You can also take precautions against failure such as these 7 pre-flight checks which you should do before letting your drone leave the ground.
Also note that recklessly endangering an aircraft in flight is a criminal offence in the UK, and anyone convicted of the charge can face a prison term. So if you live near an airport, make sure you’re flying low.
Some drones (including DJI Phantoms) have the capacity to geo-fence restricted areas, such as airports. They can also use them for ‘beginner’ modes which limit the height and distance the quadcopter can fly away from you. However, most don’t so it’s up to you to ensure you fly it safely.
First Person View & FPV racing
Since many drones have – or can be fitted with – a camera, it’s possible to buy an FPV kit and fly it using a live video stream from the camera. This is done from a video screen or special goggles, but presents a problem as you won’t have line of sight with the drone: you’re not looking directly at it.
To get around this, the FPV UK organisation worked to get an exemption for this type of drone flying and it’s legal as long as you have a ‘spotter’ who can keep the drone in their line of sight while you fly it. You can find out more at the FPVUK website
What are the drone laws in Europe?
The rules below were correct in April 2017 and are just a summary, not an exhaustive list of all regulations. Aside from a few specifics, they are much the same as the UK. In general, be sensible and don't fly over groups of people, over cities or near airports. As long as you don't endanger people, buildings or vehicles, you should be ok. But always check the latest regulations and rules in local parks before you fly.
France
Keep the drone in your line of sight and below 500ft at all times
Maintain a safe distance from people and vehicles and never fly over crowds
Don’t fly near to airfields, ensure you are at least 5km away (15km for larger sites)
No flying over ‘strategic sites’ such a power plants, national monuments or military bases without receiving prior permission
Do not fly your drone at night
Don’t use the drone’s camera to record people or vehicles without permission and never store or distribute footage without the subject’s explicit agreement
Germany
Keep the drone within sight of the pilot, (200-300m). Some areas restrict the height of such flights to between 30 and 100m, so check with local authorities.
Don't fly within 1.5km of airports
The government district in Berlin is a no-fly zone
Drones under 5kg have are exempt from specific legal aviation requirements
You need permission to fly above military installations, power plants, industrial zones, accident scenes and large crowds
Spain
Keep the drone in your line of sight and below 120m (400ft) at all times
Don't fly over groups of people at parks, beaches, concerts, processions, crowds etc
Don’t fly near to airfields or aerodromes
No flying over urban zones, such as cities
Do not fly at night
Here's a helpful map of Spain's no-fly zones
Italy
Fly below 230ft
Keep the drone within 490ft horizontally
You may not fly your drone over densely populated areas, crowds, beaches, national parks, railways, roads or industrial plants
Fly at least 8km away from aerodromes
Do not fly at night
You must fly at least 50m away from people or property
The rules for what electronic devices you can take to and from the UK have changed slightly.
We break down all the changes here, but before you travel, if you are in any way unsure about the rules for your specific airline you should check with it directly.
Always check before you fly, but also use your common sense when considering what you can and can’t travel with.
Changes affecting flights to the UK
If you’re travelling to the UK from:
Turkey
Lebanon
Jordan
Egypt
Tunisia
Saudi Arabia
Then:
All smartphones, tablets, laptops or any other electronics device larger than 16cm x 9.3cm x 1.5cm must be stowed in your hold luggage.
Don’t worry, it means popular larger phones such as the iPhone 7 Plus and Samsung Galaxy S7 edge are still OK under these rules.
If you are travelling with just hand luggage then you will not be allowed to take any device that falls into the above category on the plane with you.
Also bear in mind other electronic items such as keyboards, spare batteries, power banks and external hard drives are not allowed in hand luggage if they are larger than 16cm x 9.3cm x 1.5cm – even if you got them in duty free before the flight.
These rules only apply to direct inbound flights to the UK – not from the UK to the countries in question.
The ban affects UK airlines British Airways, EasyJest, Jet2.com, Monarch, Thomas Cook and Thomson and overseas airlines Turkish Airlines, Pegasus Airways, Atlas-Global Airlines, Middle East Airlines, Eqyptair, Royal Jordanian, Tunis Air and Saudia.
If you’re travelling to or from the UK from any other country
The rules are different for all other countries. You can take the following devices in hand or hold luggage:
Mobile phone
Laptop
Tablet
MP3 player
Hairdryer
Straighteners
Travel iron
Electric shaver/razor
E-cigarette
Camera equipment (check with your airline for specific rules)
Charge your devices before you travel
Remember that if you take a permitted item on a plane it must be charged and you must be able to show when asked that it has power. Airlines can ban you and your device from a flight if your device has no power.
Battery rules are complicated
There are more specific rules on what kind of battery technology you can and can’t take in hand and hold luggage.
4G, LTE, LTE-A, carrier aggregation. It's all tech nonsense if you don't understand what the jargon means. Here we explain the differences between 4G and LTE so you're better equipped to choose not only the best phone, but also the best tariff.
These days, there are a lot of decisions to make when getting a new phone. Along with deciding which handset is best for you, you might also have to choose a new tariff, and that's a complex business in itself.
4G is the big buzzword you'll hear or see, but what exactly is 4G? Is it the same as LTE? In a word, no, but phone manufacturers and mobile operators love to use them interchangeably, and further muddy the waters with dumbed-down marketing materials.
In this article, we'll explain everything you need to know about 4G, the speeds you can expect to get and equip you to choose a phone and tariff that's right for you.
What is 4G?
The International Telecommunications Union-Radio (ITU-R) is the United Nations official agency for all manner of information and communication technologies, which decided on the specifications for the 4G standard in March 2008.
It decided that the peak download speeds for 4G should be 100Mbit/s for high mobility devices, such as when you're using a phone in a car or on a train.
When you're stationary, (low-mobility local wireless access) it decided that 4G should be able to deliver speeds up to around 1Gbit/s.
If true 4G is supposed to offer us download speeds of up to 1Gbit/s, then why are we getting 100x less in the UK, at around 10-12Mbit/s in real-world speeds?
Unfortunately the ITU-R doesn’t have control over the implementation of the standard, which led to first-generation technologies like LTE being criticised for not being up to scratch with true 4G. (We'll explain LTE in a minute.)
The reason for this is that other groups (3GPP being an example) that work with the technology companies who develop the hardware had already decided upon next-gen technologies, leaving us with sub-standard 4G capabilities.
What is LTE?
Though originally marketed as 4G technology, LTE (Long Term Evolution) didn't satisfy the technical requirements that the ITU-R outlined, meaning that many early tariffs sold as 4G weren't actually 4G.
However due to marketing pressures and the significant advancements that LTE brings to original 3G technologies, the ITU later decided that LTE could be called 4G technology.
So, LTE is a first-generation 4G technology that should theoretically reach speeds of around 100Mbit/s. Unfortunately, Ofcom reports that the UK average is around 15.1Mbit/s. While that's around twice the speed of an average 3G connection, it’s a long way off from the theoretical top speed of LTE.
As well as lacking in overall download speed, LTE also lacks uplink spectral efficiency and speed. Uplink spectral efficiency refers to the efficiency of the rate that data is uploaded and transmitted from your smartphone.
It falls short of the true 4G capacity mainly because of the lack of carrier aggregation (explained below) and because phones don't have enough antennae.
That's where MIMO (Multiple Input Multiple Output) comes in. It's a practical technique for sending and receiving more than one data signal on the same channel at the same time by using more than one antenna.
With better carrier aggregation and MIMO, we can head towards a new standard: LTE Advanced. This is also known as 'true' 4G.
Imagine playing a PlayStation 3 when you could be playing a PlayStation 4. The PS3 isn’t necessarily too slow to use, but you’d have a better experience using the faster console, the PS4. It’s the same with LTE – LTE is the PlayStation 3 and LTE Advanced (LTE-A) is the PlayStation 4.
What is carrier aggregation?
Carrier aggregation is part of LTE-Advanced and lets operators treat multiple radio channels in different or the same frequency bands as if they were one, producing quicker speeds and enabling users to be able to perform bandwidth hogging activities like streaming HD video much faster than ever before.
Think of your wireless connection as a pipe. You can't increase the size of the pipe, but you can add a second and third pipe. Use all three simultaneously and you’ll have three times the flow rate. It’s the same concept with carrier aggregation.
Another advantage of carrier aggregation is that speeds don’t decrease, no matter how far away from the cell tower you are.
Combining two signals - or channels - should theoretically double the download speed to around 150Mbit/s. In the future, there could be aggregation across more than two channels, potentially up to five, which was defined in the LTE Advanced standard.
What about HSPA+?
HSPA+ may be marketed as 4G technology but it’s technically 3G. HSPA+ stands for High Speed Packet Access Plus. It was the next step after 3G, with UK network provider Three aiming for it to be used by 2012 (before the introduction of LTE).
The technology was developed with a theoretical top speed of 21Mb/s, which is pretty impressive for technology that doesn’t count as 4G (3G has an average speed of around 1Mb/s). However, it was quite a way away from its theoretical top speed as the average is around 4Mb/s.
Who offers the fastest 4G LTE connection?
Now you know more about what the difference is between true 4G and the 4G LTE we’re being sold, which UK network provides the best 4G LTE connection? In November 2014, Ofcom tested the 3G and 4G connections of every major provider in the UK in five cities.
The results proved that EE has the fastest 4G LTE connection, measuring in at 18.4Mb/s on average, though still far from the theoretical top speed of LTE. You can see the results in the graph below:
Research and graph by Ofcom
It’s not just the download speed that dictates overall responsiveness of a 4G connection; latency also plays an important part. A lower latency provides better responsiveness and reduced delays when using data for browsing, video calling, etc.
Surprisingly, EE wasn’t the best provider when it came to latency – that award went to Three. Ofcom reports that Three took the least time to deliver data on both 4G (47.6ms) and 3G (53.8 ms), while O2 came last with the highest levels of latency, measuring in at 62.7ms on 4G and 86.4ms on 3G.
LTE-A availability
LTE-A is already available in selected areas – Vodafone started its LTE-A rollout in Birmingham, Manchester, and London, while EE offers it in most major UK cities.
Upgrading infrastructure to support LTE-A will be a slow process and is likely to take a couple of years, much like the initial 4G rollout. You won’t automatically get LTE-A though: there are other factors that have to be taken into consideration.
The main one is compatibility. Your phone be need to support LTE-A. As with the 3G to 4G migration, many existing phones don’t have the technology to be compatible with LTE-A. The good news though is that most recent devices, especially flagships, support the tech including:
iPhone 6s onwards
iPad Pro
Blackberry Priv and Passport
Google Pixel and Pixel XL
HTC One M9, A9, and 10
Moto Z and X Style
LG G3 onwards
Huawei Honor 6, Mate 8, and P9 onwards
OnePlus 2 onwards
Samsung Galaxy S5 onwards, Notes S4 onwards, and A-series
Sony Xperia X, XZ, and Z3 onwards
The good news is that it appears that both Vodafone and EE aren’t charging people for the extra speed. As long as you’re in a supported area and using a compatible phone, you should be able to enjoy the benefits of LTE-A’s carrier aggregation and see (theoretical) download speeds of around 150Mbit/s. Just watch out you don't burn through your monthly data allowance in a few minutes!
